Full Stack Engineer, AI systems

Bjak; Town of Poland (NY); On-site; USD 120,000 - 170,000; Full time

Job summary

A1 is seeking a Full Stack Engineer - AI Systems to build the product layer that enables AI-powered workflows. You will design how agents operate, fail, recover, and deliver reliable value to users across frontend, backend, and AI integrations.

Ideal candidates will have deep experience with LLMs, memory, RAG systems, and building scalable, low-latency AI features in production.

Qualifications

• Experience building AI-native product features across frontend, backend, and integrations.
• Ability to design multi-step agent workflows with planning, tool use, failure handling.
• Experience deploying LLMs and memory or RAG systems in production.
• Strong collaboration with ML, backend, and product teams.
• Willingness to iterate based on usage data and failure modes.

Responsibilities

• Build end-to-end product features across frontend, backend, and AI integrations.
• Design agent workflows that handle planning, tool use, failure, and recovery.
• Integrate LLMs and external tools into reliable systems with low latency.
• Ensure real-time AI interactions with streaming and partial results.
• Improve reliability, observability, and fallback mechanisms.
• Collaborate with ML and product teams to ship features end-to-end.
• Iterate based on real-world usage and failure modes.

Skills

Full stack engineering

System design & API architecture

LLMs / AI systems

Ambiguity tolerance

Ownership / end-to-end delivery

Adaptability in fast-moving env

Tools

Next.js

Python

NodeJs

PyTorch

OpenAI/Anthropic/LLMs

SQL & NoSQL

Kubernetes

Docker

Job description

A1

There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.

Role

We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users.

Focus

• Build end-to-end product features across frontend, backend, and AI integrations
• Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.
• Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions
• Design real-time AI interactions with streaming, partial results, and tight latency constraints
• Improve system reliability, observability, and fallback mechanisms
• Collaborate closely with ML, backend, and product teams to ship features end-to-end
• Continuously iterate based on real usage and failure modes

Ideal Experiences

• Strong experience in full stack engineering (frontend + backend)
• Solid understanding of system design and API architecture
• Experience working with LLMs, RAG systems, or AI-powered applications
• Ability to handle ambiguity and make pragmatic engineering decisions
• Strong ownership - able to take features from idea to production
• Comfort working in fast-moving environments with evolving requirements

Outcomes

• Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows
• Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions
• Reduce latency and improve responsiveness of AI interactions while maintaining output quality
• Build robust fallback and recovery mechanisms for LLM and tool failures in production environments
• Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring
• Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems
• Contribute to a product experience where AI feels proactive, consistent, and dependable over time

Tech Stack

• Next.js
• Python
• NodeJs
• Pytorch
• OpenAI / Anthropic / open-source LLMs
• SQL & noSQL
• Kubernetes
• Docker